Daniel Auclair
Impact in
- Cancer Research top 0.1%
- Cancer Genomics and Diagnostics
- Hematology top 0.2%
- Multiple Myeloma Research and Treatments
Papers in
- Hematology 73
- Multiple Myeloma Research and Treatments 72
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- Protein Degradation and Inhibitors 34
- Co-authors
- Lan Bo Chen (12 shared papers)Lauren Ambrogio (2 shared papers)Philippe Henri Lagrange (1 shared paper)George B. Mackaness (1 shared paper)Hidefumi Sasaki (8 shared papers)Michael Charles Heinrich (1 shared paper)Rachael O’Connor (1 shared paper)David A. Frank (1 shared paper)
- Journals
- Blood (33 papers)Leukemia (7 papers)Nature Genetics (6 papers)Agroforestry Systems (5 papers)Nature (5 papers)
- Partner nations
- United StatesFranceCanada
In The Last Decade
Daniel Auclair
175 papers receiving 28.1k citations
Daniel Auclair's Hit Papers
Peers
Comparison fields: 5 of 181
- Cancer Research 7.0k
- Hematology 3.9k
- Oncology 8.0k
- Molecular Biology 14.7k
- Pathology and Forensic Medicine 3.1k
Countries citing papers authored by Daniel Auclair
This map shows the geographic impact of Daniel Auclair's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Auclair with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Auclair more than expected).
Fields of papers citing papers by Daniel Auclair
This network shows the impact of papers produced by Daniel Auclair. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Auclair. The network helps show where Daniel Auclair may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Auclair, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 177 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Mutational heterogeneity in cancer and the search for new cancer-associated genes Hit paper breakdown → | 2013 | 4266 |
| 2 | BAY 43-9006 Exhibits Broad Spectrum Oral Antitumor Activity and Targets the RAF/MEK/ERK Pathway and Receptor Tyrosine Kinases Involved in Tumor Progression and Angiogenesis Hit paper breakdown → | 2004 | 3503 |
| 3 | A Landscape of Driver Mutations in Melanoma Hit paper breakdown → | 2012 | 2138 |
| 4 | The Mutational Landscape of Head and Neck Squamous Cell Carcinoma Hit paper breakdown → | 2011 | 2103 |
| 5 | Mapping the Hallmarks of Lung Adenocarcinoma with Massively Parallel Sequencing Hit paper breakdown → | 2012 | 1516 |
| 6 | Initial genome sequencing and analysis of multiple myeloma Hit paper breakdown → | 2011 | 1207 |
| 7 | Punctuated Evolution of Prostate Cancer Genomes Hit paper breakdown → | 2013 | 1004 |
| 8 | Sequence analysis of mutations and translocations across breast cancer subtypes Hit paper breakdown → | 2012 | 1000 |
| 9 | The genetic landscape of high-risk neuroblastoma Hit paper breakdown → | 2013 | 933 |
| 10 | Discovery and prioritization of somatic mutations in diffuse large B-cell lymphoma (DLBCL) by whole-exome sequencing Hit paper breakdown → | 2012 | 835 |
| 11 | Widespread Genetic Heterogeneity in Multiple Myeloma: Implications for Targeted Therapy Hit paper breakdown → | 2014 | 819 |
| 12 | Medulloblastoma exome sequencing uncovers subtype-specific somatic mutations Hit paper breakdown → | 2012 | 631 |
| 13 | Exome and whole-genome sequencing of esophageal adenocarcinoma identifies recurrent driver events and mutational complexity Hit paper breakdown → | 2013 | 621 |
| 14 | Melanoma genome sequencing reveals frequent PREX2 mutations Hit paper breakdown → | 2012 | 604 |
| 15 | Comprehensive Genomic Analysis of Rhabdomyosarcoma Reveals a Landscape of Alterations Affecting a Common Genetic Axis in Fusion-Positive and Fusion-Negative Tumors Hit paper breakdown → | 2014 | 578 |
| 16 | Whole-exome sequencing identifies a recurrent NAB2-STAT6 fusion in solitary fibrous tumors Hit paper breakdown → | 2013 | 478 |
| 17 | Whole-exome sequencing of circulating tumor cells provides a window into metastatic prostate cancer Hit paper breakdown → | 2014 | 475 |
| 18 | The Genomic Landscape of Pediatric Ewing Sarcoma Hit paper breakdown → | 2014 | 424 |
| 19 | 2018 | 376 | |
| 20 | A high-risk, Double-Hit, group of newly diagnosed myeloma identified by genomic analysis Hit paper breakdown → | 2018 | 364 |
About Daniel Auclair
Daniel Auclair is a scholar working on Hematology, Molecular Biology, Oncology, Cancer Research and Nature and Landscape Conservation, having authored 177 papers that have together received 28.8k indexed citations. Recurring topics across this work include Multiple Myeloma Research and Treatments (72 papers), Protein Degradation and Inhibitors (34 papers), Forest ecology and management (15 papers), Cancer Genomics and Diagnostics (15 papers), Peptidase Inhibition and Analysis (15 papers), Greenhouse Technology and Climate Control (12 papers), Forest Management and Policy (12 papers) and Leaf Properties and Growth Measurement (11 papers). The work is most often cited by research in Cancer Research (7.0k citations), Hematology (3.9k citations), Oncology (8.0k citations), Molecular Biology (14.7k citations) and Pathology and Forensic Medicine (3.1k citations). Daniel Auclair has collaborated with scholars based in United States, France and Canada. Frequent co-authors include Lan Bo Chen, Lauren Ambrogio, Philippe Henri Lagrange, George B. Mackaness, Hidefumi Sasaki, Michael Charles Heinrich, Rachael O’Connor, David A. Frank, Aaron McKenna and Jonathan J. Keats. Their work appears in journals such as Blood, Leukemia, Nature Genetics, Agroforestry Systems and Nature.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.